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-<!DOCTYPE html><html><head><title>Engine Template Gallery</title><meta 
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class="level-2"><a class="final" 
href="/datacollection/eventapi/"><span>Collecting Data with 
REST/SDKs</span></a></li><li class="level-2"><a class="final" 
href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li 
class="level-2"><a class="final" 
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href="/datacollection/channel/"><span>Channel</span></a></li><li 
class="level-2"><a class="final" 
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href="/datacollection/plugin/"><span>Event Server 
Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Choosing an A
 lgorithm(s)</span></a><ul><li class="level-2"><a class="final" 
href="/algorithm/"><span>Built-in Algorithm Libraries</span></a></li><li 
class="level-2"><a class="final" href="/algorithm/switch/"><span>Switching to 
Another Algorithm</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/multiple/"><span>Combining Multiple 
Algorithms</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/custom/"><span>Adding Your Own 
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class="final" href="/evaluation/"><span>Overview</span></a></li><li 
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href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricchoose/"><span>Choosing Evaluation Met
 rics</span></a></li><li class="level-2"><a class="final" 
href="/evaluation/metricbuild/"><span>Building Evaluation 
Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" 
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class="final" href="/system/"><span>Architecture Overview</span></a></li><li 
class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using 
Another Data Store</span></a></li></ul></li><li class="level-1"><a 
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Templates</span></a><ul><li class="level-2"><a class="final" 
href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 
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class="level-3"><a class="final" 
href="/templates/recommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" href="/tem
 plates/recommendation/evaluation/"><span>Evaluation 
Explained</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
Events</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
Preparator</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/customize-serving/"><span>Customize 
Serving</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/training-with-implicit-preference/"><span>Train 
with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/batch-evaluator/"><span>Batch Pers
 istable Evaluator</span></a></li></ul></li><li class="level-2"><a 
class="expandible" href="#"><span>E-Commerce Recommendation</span></a><ul><li 
class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a 
 class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Classification</span></a><ul><li class="level-3">
 <a class="final" href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final active" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Demo Tu
 torials</span></a><ul><li class="level-2"><a class="final" 
href="/demo/tapster/"><span>Comics Recommendation Demo</span></a></li><li 
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Contributed Demo</span></a></li><li class="level-2"><a class="final" 
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href="/community/projects
 /"><span>Community Projects</span></a></li></ul></li><li class="level-1"><a 
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href="/cli/"><span>Command-line Interface</span></a></li><li class="level-2"><a 
class="final" href="/resources/release/"><span>Release 
Cadence</span></a></li><li class="level-2"><a class="final" 
href="/resources/intellij/"><span>Developing Engines with IntelliJ 
IDEA</span></a></li><li class="level-2"><a class="final" 
href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li 
class="level-2"><a class="final" 
href="/resources/glossary/"><span>Glossary</span></a></li></ul></li><li 
class="level-1"><a class="expandible" href="#"><span>Apache Softwa
 re Foundation</span></a><ul><li class="level-2"><a class="final" 
href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li 
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Engine Template Gallery</a><span 
class="spacer">&gt;</span></li><li><span 
class="last">Browse</span></li></ul></div><div id="page-title"><h1>Engine 
Template Gallery</h1></div></div><div id="table-of-content-wrapper"><a id="edit-
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href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/gallery/template-gallery.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Engine Template 
Gallery</a><span class="spacer">&gt;</span></li><li><span 
class="last">Browse</span></li></ul></div><div id="page-title"><h1>Engine 
Template Gallery</h1></div></div><div class="content"><p>Pick a tab for the 
type of template you are looking for. Some still need to be ported (a simple 
process) to Apache PIO and these are marked. Also see each Template description 
for special support instructions.</p><div class="tabs"> <ul class="control"> 
<li data-lang=""><a 
href="#tab-0e9b7fda-e46b-4afd-96e2-af7095578e28">Recommenders</a></li> <li 
data-lang=""><a 
href="#tab-abd9c5fc-ab42-4ee9-9bd9-fcd96294e3c6">Classification</a></li> <li 
data-lang=""><a href="#tab
 -a14200a7-3042-4989-8c63-27885d325f9e">Regression</a></li> <li data-lang=""><a 
href="#tab-9ff27db5-646b-4cd4-8e37-79dcd03928c6">NLP</a></li> <li 
data-lang=""><a 
href="#tab-77aef894-a073-4ba4-9348-8775f23c6824">Clustering</a></li> <li 
data-lang=""><a 
href="#tab-b9a10419-5fc6-45fd-85fc-e36ff93f330d">Similarity</a></li> <li 
data-lang=""><a href="#tab-09effe52-8138-4cd4-a900-ddba71bcc818">Other</a></li> 
</ul> <div data-tab="Recommenders" 
id="tab-0e9b7fda-e46b-4afd-96e2-af7095578e28"> <h3><a 
href="https://github.com/actionml/universal-recommender";>The Universal 
Recommender</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=actionml&amp;repo=universal-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Use for: </p> <ul class="tab-list"> <li 
class="tab-list-element">Personalized recommendations—user-based</li> <li 
class="tab-list-element">Similar items—item-based</li> <li 
class="tab-list-element">V
 iewed this bought that—item-based cross-action</li> <li 
class="tab-list-element">Popular Items and User-defined ranking</li> <li 
class="tab-list-element">Item-set recommendations for complimentarty purchases 
or shopping carts—item-set-based</li> <li class="tab-list-element">Hybrid 
collaborative filtering and content based recommendations—limited 
content-based</li> <li class-tab-list-element>Business rules</li> </ul> <p>The 
name "Universal" refers to the use of this template in virtually any case that 
calls for recommendations - ecommerce, news, videos, virtually anywhere user 
behavioral data is known. This recommender uses the new <a 
href="http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html";>Cross-Occurrence
 (CCO) algorithm</a> to auto-correlate different user actions (clickstream 
data), profile data, contextual information (location, device), and some 
content types to make better recommendations. It also implements flexible 
filters and boosts for implement
 ing business rules.</p> <p>Support: <a 
href="https://groups.google.com/forum/#!forum/actionml-user";>The Universal 
Recommender user group</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/apache/incubator-predictionio-template-recommender";>Recommendation</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> An engine template is an almost-complete 
implementation of an engine. PredictionIO's Recommendation Engine Template has 
integrated Apache Spark MLlib's Collaborative Filtering algorithm by default. 
You ca
 n customize it easily to fit your specific needs. </p> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/apache/incubator-predictionio-template-ecom-recommender";>E-Commerce
 Recommendation</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-ecom-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template provides personalized 
recommendation for e-commerce applications with the following features by 
default: </p> <ul class="tab-list"> <li class="tab-l
 ist-element">Exclude out-of-stock items</li> <li 
class="tab-list-element">Provide recommendation to new users who sign up after 
the model is trained</li> <li class="tab-list-element">Recommend unseen items 
only (configurable)</li> <li class="tab-list-element">Recommend popular items 
if no information about the user is available (added in template version 
v0.4.0)</li> </ul> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/apache/incubator-predictionio-template-similar-product";>Similar
 Product</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-tem
 plate-similar-product&amp;type=star&amp;count=true" frameborder="0" 
align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This 
engine template recommends products that are "similar" to the input product(s). 
Similarity is not defined by user or item attributes but by users' previous 
actions. By default, it uses 'view' action such that product A and B are 
considered similar if most users who view A also view B. The template can be 
customized to support other action types such as buy, rate, like..etc </p> 
<p>Support: <a href="http://predictionio.apache.org/support/";>Apache 
PredictionIO mailing lists</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.10.0-incubating</td> <td>already compatible</td> </tr> </table> <br> 
<h3><a href="https://github.com/apache/incubator-predictio
 nio-template-java-ecom-recommender">E-Commerce Recommendation (Java)</a></h3> 
<iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-java-ecom-recommender&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template provides personalized 
recommendation for e-commerce applications with the following features by 
default: </p> <ul class="tab-list"> <li class="tab-list-element">Exclude 
out-of-stock items</li> <li class="tab-list-element">Provide recommendation to 
new users who sign up after the model is trained</li> <li 
class="tab-list-element">Recommend unseen items only (configurable)</li> <li 
class="tab-list-element">Recommend popular items if no information about the 
user is available</li> </ul> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th
 > <th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion 
 > Required</th> </tr> <tr> <td>Parallel</td> <td>Java</td> <td>Apache Licence 
 > 2.0</td> <td>alpha</td> <td>0.9.3</td> <td>requires conversion</td> </tr> 
 > </table> <br> <h3><a 
 > href="https://github.com/PredictionIO/template-scala-parallel-productranking";>Product
 >  Ranking</a></h3> <iframe 
 > src="https://ghbtns.com/github-btn.html?user=PredictionIO&amp;repo=template-scala-parallel-productranking&amp;type=star&amp;count=true";
 >  frameborder="0" align="middle" scrolling="0" width="170px" 
 > height="20px"></iframe> <p> This engine template sorts a list of products 
 > for a user based on his/her preference. This is ideal for personalizing the 
 > display order of product page, catalog, or menu items if you have large 
 > number of options. It creates engagement and early conversion by placing 
 > products that a user prefers on the top. </p> <p>Support: </p> <br> <table> 
 > <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> 
 > <th>PIO min
  version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/PredictionIO/template-scala-parallel-complementarypurchase";>Complementary
 Purchase</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=PredictionIO&amp;repo=template-scala-parallel-complementarypurchase&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template recommends the complementary 
items which most user frequently buy at the same time with one or more items in 
the query. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> 
<td>requires conve
 rsion</td> </tr> </table> <br> <h3><a 
href="https://github.com/vaibhavist/template-scala-parallel-recommendation";>Music
 Recommendations</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=vaibhavist&amp;repo=template-scala-parallel-recommendation&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is very similar to music recommendations 
template. It is integrated with all the events a music application can have 
such as song played, liked, downloaded, purchased, etc. </p> <p>Support: <a 
href="https://github.com/vaibhavist/template-scala-parallel-recommendation/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a href="https://g
 ithub.com/vngrs/template-scala-parallel-viewedthenbought">Viewed This Bought 
That</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=vngrs&amp;repo=template-scala-parallel-viewedthenbought&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This Engine uses co-occurence algorithm to match 
viewed items to bought items. Using this engine you may predict which item the 
user will buy, given the item(s) browsed. </p> <p>Support: <a 
href="https://github.com/vngrs/template-scala-parallel-viewedthenbought/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern 
Mining</a></h3
 > <iframe 
 > src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-fpm&amp;type=star&amp;count=true";
 >  frameborder="0" align="middle" scrolling="0" width="170px" 
 > height="20px"></iframe> <p> Template uses FP Growth algorithm allowing to 
 > mine for frequent patterns. Template returns subsequent items together with 
 > confidence score. Sometimes used as a shopping cart recommender but has 
 > other uses. </p> <p>Support: <a 
 > href="https://github.com/goliasz/pio-template-fpm/issues";>Github 
 > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 > Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 > <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires 
 > conversion</td> </tr> </table> <br> <h3><a 
 > href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating";>Similar
 >  Product with Rating</a></h3> <iframe 
 > src="https://ghbtns.com/github-btn.html?user=ramab
 
oo&amp;repo=template-scala-parallel-similarproduct-with-rating&amp;type=star&amp;count=true"
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Similar product template with rating support! Used 
for the MovieLens Demo. </p> <p>Support: <a 
href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/goliasz/pio-template-fpm";>Frequent Pattern 
Mining</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-fpm&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Template uses FP
  Growth algorithm allowing to mine for frequent patterns. Template returns 
subsequent items together with confidence score. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> 
<br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> 
</div> <div data-tab="Classification" 
id="tab-abd9c5fc-ab42-4ee9-9bd9-fcd96294e3c6"> <h3><a 
href="https://github.com/apache/incubator-predictionio-template-attribute-based-classifier";>Classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-attribute-based-classifier&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> An engine template is 
 an almost-complete implementation of an engine. PredictionIO's Classification 
Engine Template has integrated Apache Spark MLlib's Naive Bayes algorithm by 
default. </p> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>already compatible</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/haricharan123/PredictionIo-lingpipe-MultiLabelClassification";>Classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=haricharan123&amp;repo=PredictionIo-lingpipe-MultiLabelClassification&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template is an almost-complete 
implementation of an engi
 ne meant to used with PredictionIO. This Multi-label Classification Engine 
Template has integrated LingPipe (http://alias-i.com/lingpipe/) algorithm by 
default. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Java</td> <td>Apache Licence 2.0</td> <td>stable</td> <td>0.9.5</td> 
<td>already compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/PredictionIO/template-scala-parallel-leadscoring";>Lead 
Scoring</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=PredictionIO&amp;repo=template-scala-parallel-leadscoring&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine template predicts the probability of an 
user will convert (conversion event by user) in the current session. </p> 
<p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Langua
 ge</th> <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache 
PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
<td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires 
conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/apache/incubator-predictionio-template-text-classifier";>Text
 Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-text-classifier&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Use this engine for general text classification 
purposes. Uses OpenNLP library for text vectorization, includes 
t.f.-i.d.f.-based feature transformation and reduction, and uses Spark MLLib's 
Multinomial Naive Bayes implementation for classification. </p> <p>Support: <a 
href="https://github.com/apache/incubator-predictionio-template-text-classifier/issues";>Github
 issues</a></p> <br> <tabl
 e> <tr> <th>Type</th> <th>Language</th> <th>License</th> <th>Status</th> 
<th>PIO min version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> 
<td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/andrewwuan/PredictionIO-Churn-Prediction-H2O-Sparkling-Water";>Churn
 Prediction - H2O Sparkling Water</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=andrewwuan&amp;repo=PredictionIO-Churn-Prediction-H2O-Sparkling-Water&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is an engine template with Sparkling Water 
integration. The goal is to use Deep Learning algorithm to predict the churn 
rate for a phone carrier's customers. </p> <p>Support: <a 
href="https://github.com/andrewwuan/PredictionIO-Churn-Prediction-H2O-Sparkling-Water/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</
 th> <th>Language</th> <th>License</th> <th>Status</th> <th>PIO min 
version</th> <th>Apache PIO Convesion Required</th> </tr> <tr> 
<td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> <td>alpha</td> 
<td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/detrevid/predictionio-template-classification-dl4j";>Classification
 Deeplearning4j</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=detrevid&amp;repo=predictionio-template-classification-dl4j&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> A classification engine template that uses 
Deeplearning4j library. </p> <p>Support: <a 
href="https://github.com/detrevid/predictionio-template-classification-dl4j/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>S
 cala</td> <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> 
<td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs";>Probabilistic
 Classifier (Logistic Regression w/ LBFGS)</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=EmergentOrder&amp;repo=template-scala-probabilistic-classifier-batch-lbfgs&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> A PredictionIO engine template using logistic 
regression (trained with limited-memory BFGS ) with raw (probabilistic) 
outputs. </p> <p>Support: <a 
href="https://github.com/EmergentOrder/template-scala-probabilistic-classifier-batch-lbfgs/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>MIT Lice
 nse</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/chrischris292/template-classification-opennlp";>Document
 Classification with OpenNLP</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=chrischris292&amp;repo=template-classification-opennlp&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Document Classification template with OpenNLP 
GISModel. </p> <p>Support: <a 
href="https://github.com/chrischris292/template-classification-opennlp/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/harry5z/template-circuit-classification-sparkling-wa
 ter">Circuit End Use Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=harry5z&amp;repo=template-circuit-classification-sparkling-water&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> A classification engine template that uses machine 
learning models trained with sample circuit energy consumption data and end 
usage to predict the end use of a circuit by its energy consumption history. 
</p> <p>Support: <a 
href="https://github.com/harry5z/template-circuit-classification-sparkling-water/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.1</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/ailurus1991/GBRT_Template_PredictionIO";>GBRT_Classif
 ication</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=ailurus1991&amp;repo=GBRT_Template_PredictionIO&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> The Gradient-Boosted Regression Trees(GBRT) for 
classification. </p> <p>Support: <a 
href="https://github.com/ailurus1991/GBRT_Template_PredictionIO/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/mohanaprasad1994/PredictionIO-MLlib-Decision-Trees-Template";>MLlib-Decision-Trees-Template</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=mohanaprasad1994&amp;repo=PredictionIO-MLlib-Decision-Trees-Template&amp;type=star&amp;count=true";
 fr
 ameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> An engine template is an almost-complete 
implementation of an engine. This is a classification engine template which has 
integrated Apache Spark MLlib's Decision tree algorithm by default. </p> 
<p>Support: <a 
href="https://github.com/mohanaprasad1994/PredictionIO-MLlib-Decision-Trees-Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/jimmyywu/predictionio-template-classification-dl4j-multilayer-network";>Classification
 with MultiLayerNetwork</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jimmyywu&amp;repo=predictionio-template-classification-dl4j-multilayer-network&amp;type
 =star&amp;count=true" frameborder="0" align="middle" scrolling="0" 
width="170px" height="20px"></iframe> <p> This engine template integrates the 
MultiLayerNetwork implementation from the Deeplearning4j library into 
PredictionIO. In this template, we use PredictionIO to classify the 
widely-known IRIS flower dataset by constructing a deep-belief net. </p> 
<p>Support: <a 
href="https://github.com/jimmyywu/predictionio-template-classification-dl4j-multilayer-network/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn";>Deeplearning4j
 RNTN</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-parallel-d
 l4j-rntn&amp;type=star&amp;count=true" frameborder="0" align="middle" 
scrolling="0" width="170px" height="20px"></iframe> <p> Recursive Neural Tensor 
Network algorithm is supervised learning algorithm used to predict sentiment of 
sentences. This template is based on deeplearning4j RNTN example: 
https://github.com/SkymindIO/deeplearning4j-nlp-examples/tree/master/src/main/java/org/deeplearning4j/rottentomatoes/rntn.
 It's goal is to show how to integrate deeplearning4j library with 
PredictionIO. </p> <p>Support: <a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/singsanj/classifier-kafka-streaming-template";>cla
 ssifier-kafka-streaming-template</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=singsanj&amp;repo=classifier-kafka-streaming-template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> The template will provide a simple integration of 
DASE with kafka using spark streaming capabilites in order to play around with 
real time notification, messages .. </p> <p>Support: <a 
href="https://github.com/singsanj/classifier-kafka-streaming-template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template";>Sentiment
 Analysis - Bag of Words Model</a></h3> <iframe src="https://g
 
hbtns.com/github-btn.html?user=peoplehum&amp;repo=BagOfWords_SentimentAnalysis_Template&amp;type=star&amp;count=true"
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This sentiment analysis template uses a bag of 
words model. Given text, the engine will return sentiment as 1.0 (positive) or 
0.0 (negative) along with scores indicating how +ve or -ve it is. </p> 
<p>Support: <a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/jpioug/predictionio-template-iris";>Classification 
template for Iris</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jpiou
 g&amp;repo=predictionio-template-iris&amp;type=star&amp;count=true" 
frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is Python(PySpark) based classification 
example for Iris dataset. </p> <p>Support: </p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Python</td> <td>Apache Licence 2.0</td> <td>stable</td> 
<td>0.12.0-incubating</td> <td></td> </tr> </table> <br> </div> <div 
data-tab="Regression" id="tab-a14200a7-3042-4989-8c63-27885d325f9e"> <h3><a 
href="https://github.com/goliasz/pio-template-sr";>Survival Regression</a></h3> 
<iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-sr&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Survival regression template is based on brand new 
Spark 1.6 AFT (accelerated failure 
 time) survival analysis algorithm. There are interesting applications of 
survival analysis like: </p> <ul class="tab-list"> <li 
class="tab-list-element">Business Planning : Profiling customers who has a 
higher survival rate and make strategy accordingly.</li> <li 
class="tab-list-element">Lifetime Value Prediction : Engage with customers 
according to their lifetime value</li> <li class="tab-list-element">Active 
customers : Predict when the customer will be active for the next time and take 
interventions accordingly. * Campaign evaluation : Monitor effect of campaign 
on the survival rate of customers.</li> </ul> Source: 
http://www.analyticsvidhya.com/blog/2014/04/survival-analysis-model-you/ 
<p>Support: <a 
href="http://www.analyticsvidhya.com/blog/2014/04/survival-analysis-model-you/";>Blog
 post</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala
 </td> <td>Apache Licence 2.0</td> <td>beta</td> <td>0.9.5</td> <td>requires 
conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater";>Sparkling
 Water-Deep Learning Energy Forecasting</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=BensonQiu&amp;repo=predictionio-template-recommendation-sparklingwater&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This Engine Template demonstrates an energy 
forecasting engine. It integrates Deep Learning from the Sparkling Water 
library to perform energy analysis. We can query the circuit and time, and 
return predicted energy usage. </p> <p>Support: <a 
href="https://github.com/BensonQiu/predictionio-template-recommendation-sparklingwater/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesio
 n Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 
2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/detrevid/predictionio-load-forecasting";>Electric Load 
Forecasting</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=detrevid&amp;repo=predictionio-load-forecasting&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is a PredictionIO engine for electric load 
forecasting. The engine is using linear regression with stochastic gradient 
descent from Spark MLlib. </p> <p>Support: <a 
href="https://github.com/detrevid/predictionio-load-forecasting/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.
 9.2</td> <td>requires conversion</td> </tr> </table> <br> <h3><a 
href="https://github.com/RAditi/PredictionIO-MLLib-LinReg-Template";>MLLib-LinearRegression</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=RAditi&amp;repo=PredictionIO-MLLib-LinReg-Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template uses the linear regression with 
stochastic gradient descent algorithm from MLLib to make predictions on 
real-valued data based on features (explanatory variables) </p> <p>Support: <a 
href="https://github.com/RAditi/PredictionIO-MLLib-LinReg-Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.1</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a href="https:
 //github.com/mgcdanny/pio-linear-regression-bfgs">Linear Regression 
BFGS</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=mgcdanny&amp;repo=pio-linear-regression-bfgs&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Modeling the relationship between a dependent 
variable, y, and one or more explanatory variables, denoted X. </p> <p>Support: 
</p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>beta</td> <td>0.10.0</td> <td></td> </tr> </table> <br> <h3><a 
href="https://github.com/jpioug/predictionio-template-boston-house-prices";>Regression
 template for Boston House Prices</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=jpioug&amp;repo=predictionio-template-boston-house-prices&amp;type=star&amp;count=true";
 frameborder="0
 " align="middle" scrolling="0" width="170px" height="20px"></iframe> <p> This 
is Python(PySpark) based regression example for Boston House Prices dataset. 
</p> <p>Support: </p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Python</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.12.0-incubating</td> <td></td> </tr> 
</table> <br> </div> <div data-tab="NLP" 
id="tab-9ff27db5-646b-4cd4-8e37-79dcd03928c6"> <h3><a 
href="https://github.com/goliasz/pio-template-text-similarity";>Cstablo-template-text-similarity-classification</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-text-similarity&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Text similarity engine based on Word2Vec algorithm. 
Builds vectors of full documents in training phase. Finds 
 similar documents in query phase. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-text-similarity/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia";>Topic
 Labelling with Wikipedia</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=peoplehum&amp;repo=template-Labelling-Topics-with-wikipedia&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template will label topics (e.g. topic 
generated through LDA topic modeling) with relevant category by referring to 
Wikipedia as a knowledge base. </p> <p>Support: <a href="https://github.com/pe
 oplehum/template-Labelling-Topics-with-wikipedia/issues">Github issues</a></p> 
<br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>stable</td> <td>0.10.0-incubating</td> <td>already compatible</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/apache/incubator-predictionio-template-text-classifier";>Text
 Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-text-classifier&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Use this engine for general text classification 
purposes. Uses OpenNLP library for text vectorization, includes 
t.f.-i.d.f.-based feature transformation and reduction, and uses Spark MLLib's 
Multinomial Naive Bayes implementation for classification. </p> <p
 >Support: <a 
 >href="https://github.com/apache/incubator-predictionio-template-text-classifier/issues";>Github
 > issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 ><th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 >Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 ><td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires 
 >conversion</td> </tr> </table> <br> <h3><a 
 >href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn";>Deeplearning4j
 > RNTN</a></h3> <iframe 
 >src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-parallel-dl4j-rntn&amp;type=star&amp;count=true";
 > frameborder="0" align="middle" scrolling="0" width="170px" 
 >height="20px"></iframe> <p> Recursive Neural Tensor Network algorithm is 
 >supervised learning algorithm used to predict sentiment of sentences. This 
 >template is based on deeplearning4j RNTN example: 
 >https://github.com/SkymindIO/deeplearning4j-nlp-examples/tree/master/src/main
 /java/org/deeplearning4j/rottentomatoes/rntn. It's goal is to show how to 
integrate deeplearning4j library with PredictionIO. </p> <p>Support: <a 
href="https://github.com/thomasste/template-scala-parallel-dl4j-rntn/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template";>Sentiment
 Analysis - Bag of Words Model</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=peoplehum&amp;repo=BagOfWords_SentimentAnalysis_Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This sentiment analysis template uses a bag of 
words model. Given text, the engine will return se
 ntiment as 1.0 (positive) or 0.0 (negative) along with scores indicating how 
+ve or -ve it is. </p> <p>Support: <a 
href="https://github.com/peoplehum/BagOfWords_SentimentAnalysis_Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/infoquestsolutions/OpenNLP-SentimentAnalysis-Template";>OpenNLP
 Sentiment Analysis Template</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=infoquestsolutions&amp;repo=OpenNLP-SentimentAnalysis-Template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Given a sentence, this engine will return a score 
between 0 and 4. This is the sentiment of the sentence
 . The lower the number the more negative the sentence is. It uses the OpenNLP 
library. </p> <p>Support: <a 
href="https://github.com/infoquestsolutions/OpenNLP-SentimentAnalysis-Template/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> <h3><a 
href="https://github.com/pawel-n/template-scala-cml-sentiment";>Sentiment 
analysis</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=pawel-n&amp;repo=template-scala-cml-sentiment&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template implements various algorithms for 
sentiment analysis, most based on recursive neural networks (RNN) and recursive 
neural tensor networks (RNTN)[1
 ]. It uses an experimental library called Composable Machine Learning (CML) 
and the Stanford Parser. The example data set is the Stanford Sentiment 
Treebank. </p> <p>Support: <a 
href="https://github.com/pawel-n/template-scala-cml-sentiment/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/pawel-n/template-scala-parallel-word2vec";>Word2Vec</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=pawel-n&amp;repo=template-scala-parallel-word2vec&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template integrates the Word2Vec 
implementation from deeplearning4j with PredictionIO. The Word2Vec algorithm ta
 kes a corpus of text and computes a vector representation for each word. These 
representations can be subsequently used in many natural language processing 
applications. </p> <p>Support: <a 
href="https://github.com/pawel-n/template-scala-parallel-word2vec/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/thomasste/template-scala-spark-dl4j-word2vec";>Spark 
Deeplearning4j Word2Vec</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-spark-dl4j-word2vec&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template shows how to integrate Deeplearnign4j 
spark api with Predi
 ctionIO on example of app which uses Word2Vec algorithm to predict nearest 
words. </p> <p>Support: <a 
href="https://github.com/thomasste/template-scala-spark-dl4j-word2vec/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/whhone/template-sentiment-analysis";>Sentiment Analysis 
Template</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=whhone&amp;repo=template-sentiment-analysis&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Given a sentence, return a score between 0 and 4, 
indicating the sentence's sentiment. 0 being very negative, 4 being very 
positive, 2 being neutral. The engine uses the stanfor
 d CoreNLP library and the Scala binding `gangeli/CoreNLP-Scala` for parsing. 
</p> <p>Support: <a 
href="https://github.com/whhone/template-sentiment-analysis/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
<td>None</td> <td>stable</td> <td>0.9.0</td> <td>requires conversion</td> </tr> 
</table> <br> <h3><a 
href="https://github.com/thomasste/template-scala-rnn";>Recursive Neural 
Networks (Sentiment Analysis)</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=thomasste&amp;repo=template-scala-rnn&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Predicting sentiment of phrases with use of 
Recursive Neural Network algorithm and OpenNLP parser. </p> <p>Support: <a 
href="https://github.com/thomasste/template-scala-rnn/issues";>Github issues</a><
 /p> <br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>stable</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a href="https://github.com/Ling-Ling/CoreNLP-Text-Classification";>CoreNLP 
Text Classification</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=Ling-Ling&amp;repo=CoreNLP-Text-Classification&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This engine uses CoreNLP to do text analysis in 
order to classify the category a strings of text falls under. </p> <p>Support: 
<a 
href="https://github.com/Ling-Ling/CoreNLP-Text-Classification/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th>
  </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> </table> <br> 
</div> <div data-tab="Clustering" 
id="tab-77aef894-a073-4ba4-9348-8775f23c6824"> <h3><a 
href="https://github.com/sahiliitm/predictionio-MLlibKMeansClusteringTemplate";>MLlibKMeansClustering</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=sahiliitm&amp;repo=predictionio-MLlibKMeansClusteringTemplate&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This is a template which demonstrates the use of 
K-Means clustering algorithm which can be deployed on a spark-cluster using 
prediction.io. </p> <p>Support: <a 
href="https://github.com/sahiliitm/predictionio-MLlibKMeansClusteringTemplate/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr
 > <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
 > <td>alpha</td> <td>-</td> <td>requires conversion</td> </tr> </table> <br> 
 > <h3><a 
 > href="https://github.com/EmergentOrder/template-scala-topic-model-LDA";>Topc 
 > Model (LDA)</a></h3> <iframe 
 > src="https://ghbtns.com/github-btn.html?user=EmergentOrder&amp;repo=template-scala-topic-model-LDA&amp;type=star&amp;count=true";
 >  frameborder="0" align="middle" scrolling="0" width="170px" 
 > height="20px"></iframe> <p> A PredictionIO engine template using Latent 
 > Dirichlet Allocation to learn a topic model from raw text </p> <p>Support: 
 > <a 
 > href="https://github.com/EmergentOrder/template-scala-topic-model-LDA/issues";>Github
 >  issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 > <th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 > Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 > <td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.4</td> <td>requires 
 > conversion</td> </tr> </table> <br> <h3><a
  
href="https://github.com/singsanj/KMeans-parallel-template";>KMeans-Clustering-Template</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=singsanj&amp;repo=KMeans-parallel-template&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> forked from 
PredictionIO/template-scala-parallel-vanilla. It implements the KMeans 
Algorithm. Can be extended to mainstream implementation with minor changes. 
</p> <p>Support: <a 
href="https://github.com/singsanj/KMeans-parallel-template/issues";>Github 
issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>alpha</td> <td>0.9.2</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia";>Topic
 Labelling with Wikipedia</a></h
 3> <iframe 
src="https://ghbtns.com/github-btn.html?user=peoplehum&amp;repo=template-Labelling-Topics-with-wikipedia&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> This template will label topics (e.g. topic 
generated through LDA topic modeling) with relevant category by referring to 
Wikipedia as a knowledge base. </p> <p>Support: <a 
href="https://github.com/peoplehum/template-Labelling-Topics-with-wikipedia/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.10.0-incubating</td> <td>already 
compatible</td> </tr> </table> <br> </div> <div data-tab="Similarity" 
id="tab-b9a10419-5fc6-45fd-85fc-e36ff93f330d"> <h3><a 
href="https://github.com/alexice/template-scala-parallel-svd-item-similarity";>Content
 Ba
 sed SVD Item Similarity Engine</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=alexice&amp;repo=template-scala-parallel-svd-item-similarity&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Template to calculate similarity between items 
based on their attributes—sometimes called content-based similarity. 
Attributes can be either numeric or categorical in the last case it will be 
encoded using one-hot encoder. Algorithm uses SVD in order to reduce data 
dimensionality. Cosine similarity is now implemented but can be easily extended 
to other similarity measures. </p> <p>Support: <a 
href="https://groups.google.com/forum/#!forum/actionml-user";>The Universal 
Recommender user group</a></p> <br> <table> <tr> <th>Type</th> 
<th>Language</th> <th>License</th> <th>Status</th> <th>PIO min version</th> 
<th>Apache PIO Convesion Required</th> </tr> <tr> <td>Parallel</td> 
<td>Scala</td> <td>Apache Licence 2.0</td> <td
 >alpha</td> <td>0.9.2</td> <td>requires conversion</td> </tr> </table> <br> 
 ><h3><a 
 >href="https://github.com/goliasz/pio-template-text-similarity";>Cstablo-template-text-similarity-classification</a></h3>
 > <iframe 
 >src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-text-similarity&amp;type=star&amp;count=true";
 > frameborder="0" align="middle" scrolling="0" width="170px" 
 >height="20px"></iframe> <p> Text similarity engine based on Word2Vec 
 >algorithm. Builds vectors of full documents in training phase. Finds similar 
 >documents in query phase. </p> <p>Support: <a 
 >href="https://github.com/goliasz/pio-template-text-similarity/issues";>Github 
 >issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
 ><th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
 >Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> 
 ><td>Apache Licence 2.0</td> <td>alpha</td> <td>0.9.5</td> <td>requires 
 >conversion</td> </tr> </table> <br> <h3><a href="https://git
 hub.com/ramaboo/template-scala-parallel-similarproduct-with-rating">Similar 
Product with Rating</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=ramaboo&amp;repo=template-scala-parallel-similarproduct-with-rating&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Similar product template with rating support! Used 
for the MovieLens Demo. </p> <p>Support: <a 
href="https://github.com/ramaboo/template-scala-parallel-similarproduct-with-rating/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>beta</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> </div> <div data-tab="Other" 
id="tab-09effe52-8138-4cd4-a900-ddba71bcc818"> <h3><a 
href="https://github.com/goliasz/pio-template-fpm";>Frequent Patte
 rn Mining</a></h3> <iframe 
src="https://ghbtns.com/github-btn.html?user=goliasz&amp;repo=pio-template-fpm&amp;type=star&amp;count=true";
 frameborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Template uses FP Growth algorithm allowing to mine 
for frequent patterns. Template returns subsequent items together with 
confidence score. </p> <p>Support: <a 
href="https://github.com/goliasz/pio-template-fpm/issues";>Github issues</a></p> 
<br> <table> <tr> <th>Type</th> <th>Language</th> <th>License</th> 
<th>Status</th> <th>PIO min version</th> <th>Apache PIO Convesion Required</th> 
</tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache Licence 2.0</td> 
<td>alpha</td> <td>0.9.5</td> <td>requires conversion</td> </tr> </table> <br> 
<h3><a 
href="https://github.com/anthill/template-decision-tree-feature-importance";>template-decision-tree-feature-importance</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=anthill&amp;repo=template-decision-tree-feature-i
 mportance&amp;type=star&amp;count=true" frameborder="0" align="middle" 
scrolling="0" width="170px" height="20px"></iframe> <p> This template shows how 
to use spark' decision tree. It enables : - both categorical and continuous 
features - feature importance calculation - tree output in json - reading 
training data from a csv file </p> <p>Support: <a 
href="https://github.com/anthill/template-decision-tree-feature-importance/issues";>Github
 issues</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.0</td> <td>requires conversion</td> 
</tr> </table> <br> <h3><a 
href="https://github.com/apache/incubator-predictionio-template-skeleton";>Skeleton</a></h3>
 <iframe 
src="https://ghbtns.com/github-btn.html?user=apache&amp;repo=incubator-predictionio-template-skeleton&amp;type=star&amp;count=true";
 fra
 meborder="0" align="middle" scrolling="0" width="170px" 
height="20px"></iframe> <p> Skeleton template is for developing new engine when 
you find other engine templates do not fit your needs. This template provides a 
skeleton to kick start new engine development. </p> <p>Support: <a 
href="http://predictionio.apache.org/support/";>Apache PredictionIO mailing 
lists</a></p> <br> <table> <tr> <th>Type</th> <th>Language</th> 
<th>License</th> <th>Status</th> <th>PIO min version</th> <th>Apache PIO 
Convesion Required</th> </tr> <tr> <td>Parallel</td> <td>Scala</td> <td>Apache 
Licence 2.0</td> <td>stable</td> <td>0.9.2</td> <td>already compatible</td> 
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Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li><li 
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Explained</span></a></li><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
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Events</span></a></li><li class="level-3"><a class="final" 
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Preparator</span></a></li><li class="level-3"><a class="final" 
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Serving</span></a></li><li class="level-3"><a class="final" 
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with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" 
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 istable Evaluator</span></a></li></ul></li><li class="level-2"><a 
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Start</span></a></li><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a 
 class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Classification</span></a><ul><li class="level-3">
 <a class="final" href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final active" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Demo Tu
 torials</span></a><ul><li class="level-2"><a class="final" 
href="/demo/tapster/"><span>Comics Recommendation Demo</span></a></li><li 
class="level-2"><a class="final" href="/demo/community/"><span>Community 
Contributed Demo</span></a></li><li class="level-2"><a class="final" 
href="/demo/textclassification/"><span>Text Classification Engine 
Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="/community/"><span>Getting Involved</span></a><ul><li class="level-2"><a 
class="final" href="/community/contribute-code/"><span>Contribute 
Code</span></a></li><li class="level-2"><a class="final" hre

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